This project uses PyTorch to build and train a neural network for classifying iris flowers based on their features. The model is designed with two hidden layers, and it leverages ReLU activations for non-linearity. The training process includes backpropagation and optimization using Adam. After training, the model's accuracy is evaluated on a test set, achieving strong performance.
- PyTorch implementation with custom model architecture
- Two hidden layers with ReLU activations
- Cross-entropy loss and Adam optimizer
- Evaluation of model accuracy on the test set
The model achieves around 96% accuracy on the test set.
- PyTorch
- Pandas
- Scikit-learn
- Matplotlib